Slippage occurs because markets don’t stand still. When a trader places an order—especially a market order—the price can move before execution, resulting in a fill that’s slightly better or worse than expected. In fast markets, even milliseconds matter. A sudden surge in buying or selling can push prices away from where a trader intended to enter or exit.
Liquidity also plays a big role. If there aren’t enough buyers or sellers at the quoted price, the order spills into the next available prices on the order book. This creates slippage, often noticeable in thinly traded stocks, after-hours sessions, or volatile events like earnings releases and economic data drops.
Slippage affects risk management and strategy performance. Traders who rely on precise entries and exits—like day traders or algorithmic systems—must account for slippage in their models. Ignoring it can turn a profitable strategy into a losing one, especially over large volumes or frequent trades.
Slippage matters because it changes the actual cost of trading. Over time, unexpected execution differences can reduce returns, distort signals, and weaken trading strategies—especially those with tight margins.
During volatile periods, prices move rapidly as traders react to news or economic events. When prices shift between the moment an order is placed and executed, slippage occurs. Highly volatile markets often see wider bid–ask spreads and faster quote changes, increasing the likelihood and severity of slippage.
Low liquidity means fewer shares or contracts are available at each price level. Large or even medium-sized orders may consume available liquidity and move up or down the order book to fill completely. This causes the execution price to slip away from the expected price.
Traders often use limit orders instead of market orders, trade during high-liquidity hours, and avoid entering positions right before major news events. Algorithmic traders may break large orders into smaller pieces or use smart order routing to find the best liquidity across venues.
A trader expects to buy a stock at $50 based on the quote they see. But during a sudden spike in buying activity, the trade executes at $50.30 instead. That 30-cent difference is slippage—and repeated over many trades, it can add up significantly.
FinFeedAPI’s Stock API is the best match for analyzing slippage because it provides detailed historical and intraday price data that traders use to simulate realistic trade execution. Developers can study bid–ask spreads, liquidity conditions, and rapid price movements to build more accurate backtests and execution models.
